Thurmon E Lockhart ^*

• School of Biological and Health Systems Engineering, Arizona State University, Tempe, VA, United States

Falls and gait instability are among the most serious problems facing older adults and constitute a major cause of mortality, morbidity, immobility, and premature nursing home placement. Fall risk assessment can be a powerful tool for early diagnosis and treatment of falls if done correctly. However, mobility impairment and associated fall risk which can be subtle is often missed, further increasing the risk of a fall. Early detection of impaired mobility and increased fall risk are thus critical to timely interventions prior to falling episodes.In recent years, many technologies for mobility, balance, and fall detection in various populations have emerged. Wearable sensors, passive in-house monitors, and many combinations thereof all promise to alert caregivers or emergency personnel once a fall is detected. But for many individuals, detecting a fall once it has occurred is already too late -the damage has been done, outcomes are typically poor, and cost is always high. What is needed for health preservation is fall prevention. While falls are a major problem that is growing as the population is rapidly ageing, most of the current technology and care solutions are based on a onedimensional approach. However, the causes leading to falls are varied and are better understood by accessing information at different scales and populations.To our knowledge, there has been no previous effort to address fall prevention from multiple levels of monitoring and predictive perspectives. In this Research Topic, we present a few studies by researchers who are at the helm of concurrently measuring multiple functions among older adults as well as those with pathology. Especially those involved in fall risk assessments and fall risk prediction areas, including balance and mobility.Although biomechanical and physiological parameters associated with mobility issues and fall risk have been established by testing/collecting cohort relevant data at the population levels, currently, it is unknown, how these features can be used for personalized assessments in international environments. The fundamental contribution of this Research Topic is to provide cogent features/models relevant for health assessments utilizing multimodal, time varying physiological and biomechanical fall risk characteristics utilizing a variety of subjective and objective techniques. In this study, Authors introduced a Mobile Technology-Based Fall Risk Health Application that can assess fall risk of special populations' fall risk -e.g., individuals with Multiple Sclerosis (MS) and Wheeled device users. Authors assert that mobile technology can be leveraged to provide personalized fall risk screening for different clinical populations. Additionally, they indicated that fall risk applications should be designed to tailor one's specific stability weaknesses to be measured and intervened.